Python Pandas How to assign groupby operation results back to columns in parent dataframe?

I have the following data frame in IPython, where each row is a single stock:

    In [261]: bdata
    Out[261]:
    <class 'pandas.core.frame.DataFrame'>
    Int64Index: 21210 entries, 0 to 21209
    Data columns:
    BloombergTicker      21206  non-null values
    Company              21210  non-null values
    Country              21210  non-null values
    MarketCap            21210  non-null values
    PriceReturn          21210  non-null values
    SEDOL                21210  non-null values
    yearmonth            21210  non-null values
    dtypes: float64(2), int64(1), object(4)

I want to apply a groupby operation that computes cap-weighted average return across everything, per each date in the "yearmonth" column.

This works as expected:

    In [262]: bdata.groupby("yearmonth").apply(lambda x: (x["PriceReturn"]*x["MarketCap"]/x["MarketCap"].sum()).sum())
    Out[262]:
    yearmonth
    201204      -0.109444
    201205      -0.290546

But then I want to sort of "broadcast" these values back to the indices in the original data frame, and save them as constant columns where the dates match.

    In [263]: dateGrps = bdata.groupby("yearmonth")

    In [264]: dateGrps["MarketReturn"] = dateGrps.apply(lambda x: (x["PriceReturn"]*x["MarketCap"]/x["MarketCap"].sum()).sum())
    ---------------------------------------------------------------------------
    TypeError                                 Traceback (most recent call last)
    /mnt/bos-devrnd04/usr6/home/espears/ws/Research/Projects/python-util/src/util/<ipython-input-264-4a68c8782426> in <module>()
    ----> 1 dateGrps["MarketReturn"] = dateGrps.apply(lambda x: (x["PriceReturn"]*x["MarketCap"]/x["MarketCap"].sum()).sum())

    TypeError: 'DataFrameGroupBy' object does not support item assignment

I realize this naive assignment should not work. But what is the "right" Pandas idiom for assigning the result of a groupby operation into a new column on the parent dataframe?

In the end, I want a column called "MarketReturn" than will be a repeated constant value for all indices that have matching date with the output of the groupby operation.

One hack to achieve this would be the following:

    marketRetsByDate  = dateGrps.apply(lambda x: (x["PriceReturn"]*x["MarketCap"]/x["MarketCap"].sum()).sum())

    bdata["MarketReturn"] = np.repeat(np.NaN, len(bdata))

    for elem in marketRetsByDate.index.values:
        bdata["MarketReturn"][bdata["yearmonth"]==elem] = marketRetsByDate.ix[elem]

But this is slow, bad, and unPythonic.

    In [97]: df = pandas.DataFrame({'month': np.random.randint(0,11, 100), 'A': np.random.randn(100), 'B': np.random.randn(100)})

    In [98]: df.join(df.groupby('month')['A'].sum(), on='month', rsuffix='_r')
    Out[98]:
               A         B  month       A_r
    0  -0.040710  0.182269      0 -0.331816
    1  -0.004867  0.642243      1  2.448232
    2  -0.162191  0.442338      4  2.045909
    3  -0.979875  1.367018      5 -2.736399
    4  -1.126198  0.338946      5 -2.736399
    5  -0.992209 -1.343258      1  2.448232
    6  -1.450310  0.021290      0 -0.331816
    7  -0.675345 -1.359915      9  2.722156

From: stackoverflow.com/q/12200693